Artificial neural network classifier predicts neuroblastoma patients' outcome. Issue 12 (October 2016)

• Record Type: Journal Article
• Authors: Cangelosi, Davide; Pelassa, Simone; Morini, Martina; Conte, Massimo; Bosco, Maria; Eva, Alessandra; Sementa, Angela; Varesio, Luigi
• Is Part Of: BMC bioinformatics. Volume 17:Issue 12(2016)
• Page Start: 83
• Page End: 93
• Publication Date: 2016-10
• Subjects: Neuroblastoma -- Hypoxia -- Outcome prediction -- Gene set enrichment analysis -- Gene signature; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1194-3
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Methods Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. Results We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis. Conclusions We developed a robust classifier predicting neuroblastoma patients' outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.